'CXXABI_1.3.8' not found in tensorflow-gpu - install from source

TensorFlow

GPU

CXXABI_1.3.8

Installation

Troubleshooting

'CXXABI_1.3.8' not found in tensorflow-gpu - install from source

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Introduction

The CXXABI_1.3.8 not found error occurs when TensorFlow's compiled binaries require a newer C++ ABI (Application Binary Interface) version than what your system's libstdc++ provides. CXXABI_1.3.8 is provided by GCC 4.9+ and its associated libstdc++.so.6. The fix is to upgrade your GCC/libstdc++ version, set LD_LIBRARY_PATH to a newer libstdc++, or build TensorFlow from source with your existing compiler.

The Error

bash

$ python -c "import tensorflow"
ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version 'CXXABI_1.3.8' not found
(required by /path/to/tensorflow/python/_pywrap_tensorflow_internal.so)

This means the TensorFlow binary was compiled with a newer GCC than what your system has installed.

Diagnosing the Problem

bash

1# Check which CXXABI versions your system supports
2strings /usr/lib/x86_64-linux-gnu/libstdc++.so.6 | grep CXXABI
3# CXXABI_1.3
4# CXXABI_1.3.1
5# ...
6# CXXABI_1.3.7    ← If this is the highest, you're missing 1.3.8
7
8# Check your GCC version
9gcc --version
10# gcc 4.8.5 ← Too old, needs 4.9+ for CXXABI_1.3.8
11
12# CXXABI version → minimum GCC version mapping:
13# CXXABI_1.3.7  → GCC 4.8
14# CXXABI_1.3.8  → GCC 4.9
15# CXXABI_1.3.9  → GCC 5.1
16# CXXABI_1.3.11 → GCC 7.1
17# CXXABI_1.3.13 → GCC 10.1

Fix 1: Upgrade GCC and libstdc++

bash

1# Ubuntu/Debian — install a newer GCC
2sudo apt update
3sudo apt install gcc-9 g++-9 libstdc++-9-dev
4
5# Or use a PPA for the latest GCC
6sudo add-apt-repository ppa:ubuntu-toolchain-r/test
7sudo apt update
8sudo apt install gcc-11 g++-11
9
10# Set as default
11sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100
12sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 100
13
14# Verify
15strings /usr/lib/x86_64-linux-gnu/libstdc++.so.6 | grep CXXABI_1.3.8
16# CXXABI_1.3.8   ← Now available

bash

1# CentOS/RHEL — use devtoolset
2sudo yum install centos-release-scl
3sudo yum install devtoolset-9
4
5# Activate
6scl enable devtoolset-9 bash
7
8# Or add to .bashrc for permanent use
9echo "source /opt/rh/devtoolset-9/enable" >> ~/.bashrc

Fix 2: Point LD_LIBRARY_PATH to a Newer libstdc++

If you have a newer GCC installed but the system still uses the old libstdc++:

bash

1# Find the newer libstdc++
2find / -name "libstdc++.so.6*" 2>/dev/null
3# /opt/rh/devtoolset-9/root/usr/lib/gcc/x86_64-linux-gnu/9/libstdc++.so.6
4# /usr/lib/x86_64-linux-gnu/libstdc++.so.6   ← old one
5
6# Point to the newer version
7export LD_LIBRARY_PATH=/opt/rh/devtoolset-9/root/usr/lib/gcc/x86_64-linux-gnu/9:$LD_LIBRARY_PATH
8
9# Or if you installed GCC via conda
10export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
11
12# Verify
13python -c "import tensorflow; print(tensorflow.__version__)"

Add the export to your .bashrc or .zshrc to make it permanent.

Fix 3: Install via Conda (Bundles libstdc++)

Conda installs its own libstdc++ that includes the required CXXABI versions:

bash

1# Create a fresh environment
2conda create -n tf python=3.10
3conda activate tf
4
5# Install TensorFlow — conda bundles compatible libstdc++
6conda install -c conda-forge tensorflow-gpu
7
8# Verify
9python -c "import tensorflow as tf; print(tf.__version__)"

Fix 4: Build TensorFlow from Source

Build TensorFlow with your system's compiler to match the available CXXABI:

bash

1# Install Bazel (TensorFlow's build system)
2# Check https://www.tensorflow.org/install/source for compatible versions
3
4# Clone TensorFlow
5git clone https://github.com/tensorflow/tensorflow.git
6cd tensorflow
7git checkout v2.15.0  # Choose your version
8
9# Configure — answer prompts for CUDA, cuDNN, etc.
10./configure
11
12# Build the pip package
13bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
14
15# Create and install the wheel
16./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
17pip install /tmp/tensorflow_pkg/tensorflow-*.whl

Building from source ensures TensorFlow links against your system's libstdc++ and avoids the CXXABI mismatch entirely.

Fix 5: Use Docker (Avoids System Issues)

bash

1# Official TensorFlow GPU image includes all dependencies
2docker run --gpus all -it tensorflow/tensorflow:latest-gpu bash
3
4# Inside the container
5python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Verifying the Fix

bash

1# Check that the required CXXABI is available
2python -c "
3import ctypes
4lib = ctypes.CDLL('libstdc++.so.6')
5print('libstdc++ loaded successfully')
6"
7
8# Full TensorFlow test
9python -c "
10import tensorflow as tf
11print(f'TensorFlow version: {tf.__version__}')
12print(f'GPU available: {tf.config.list_physical_devices(\"GPU\")}')
13print(f'Built with CUDA: {tf.test.is_built_with_cuda()}')
14"

Common Pitfalls

Upgrading GCC without updating libstdc++: Installing a new GCC compiler does not always install the matching libstdc++.so.6. On CentOS/RHEL, devtoolset installs the library in a non-standard path that is not on LD_LIBRARY_PATH by default. Run scl enable devtoolset-N bash or set LD_LIBRARY_PATH explicitly.
Mixing Conda and system libraries: When using Conda with TensorFlow, the system's libstdc++ may take precedence over Conda's newer version. Set LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH to ensure Conda's libraries are found first.
Not cleaning previous builds: When building TensorFlow from source after a failed attempt, stale build artifacts can cause linking errors. Run bazel clean --expunge before rebuilding to start fresh.
Wrong CUDA/cuDNN versions: Building from source requires specific CUDA and cuDNN versions for each TensorFlow version. Check the official build compatibility table at tensorflow.org/install/source. Mismatched versions cause cryptic linker errors beyond the CXXABI issue.
Copying libstdc++.so.6 from another system: Copying a libstdc++.so.6 binary from a different machine may not work due to glibc version mismatches. The library must be compatible with your system's glibc. Install the matching libstdc++ package instead of copying binaries.

Summary

CXXABI_1.3.8 not found means your libstdc++ is too old for the TensorFlow binary
Upgrade GCC to 4.9+ (for CXXABI_1.3.8) or 5.1+ (for CXXABI_1.3.9) and install the matching libstdc++
Set LD_LIBRARY_PATH to point to the newer libstdc++.so.6 if it is installed in a non-standard location
Use Conda to install TensorFlow with bundled compatible libraries
Build TensorFlow from source to match your system's exact compiler version
Use Docker (tensorflow/tensorflow:latest-gpu) to avoid all system dependency issues